Abstract
PURPOSE: Immune checkpoint blockade (ICB) benefits only a subset of sarcoma patients. Biomarkers of response and resistance are needed to help guide patient selection. PATIENTS AND METHODS: We analyzed peripheral blood and tumor samples from sarcoma patients treated on five ICB-based clinical trials. Baseline peripheral blood mononuclear cells (PBMCs) underwent 11-color flow cytometry to define T cell immunotypes. Baseline tumor tissue underwent RNA sequencing to classify tumors into four tumor microenvironment (TME) subtypes using consensus clustering of 29 functional gene expression signatures. Associations between immune features and clinical outcomes were assessed. A deep-learning model was applied to baseline hematoxylin and eosin (H&E) slides to detect and quantify lymphoid aggregates in patients with available RNA sequencing. RESULTS: Among 178 patients with PBMCs available for analysis, a proliferative (PRO) circulating T cell immunotype was associated with inferior overall survival (OS) compared with LAG- or LAG+ immunotypes. RNA sequencing from 67 tumors identified an immune-enriched/non-fibrotic TME subtype associated with higher response rate, longer progression-free survival, and longer OS compared to immune-enriched/fibrotic, immune-depleted, and fibrotic subtypes. Automated analysis of 48 baseline H&E slides identified lymphoid aggregates in five tumors; four were classified as immune-enriched and two responded to ICB. CONCLUSIONS: Sarcoma patients with a PRO circulating T cell immunotype had inferior outcomes to ICB, while those with an immune-enriched/non-fibrotic TME had superior outcomes. Automated analysis of H&E slides showed promise in identifying patients with an immune-enriched TME. These findings support utilization of a multimodal approach toward identifying predictors of response to immunotherapy in sarcoma.